Biasis a common risk acrossboth proprietary and open-source models, andnot uniqueto proprietary deployments. All AI systems - regardless of origin - require evaluation for fairness, accuracy, and representativeness.
From theAI Governance in Practice Report 2024:
"Bias, discrimination and fairness challenges are present in both open and closed models, regardless of how the model is sourced." (p. 41)

The greatest risk from AI systems generatingrealistic synthetic mediaisdownstream harm, such as deepfakes, misinformation, reputational damage, and erosion of trust.
From theAI Governance in Practice Report 2024:
"With generative AI, downstream harms such as deception, reputational damage, misinformation, and manipulation can emerge even if original use was lawful." (p. 55-56)

Anonymization is a privacy-enhancing technique that involves removing or permanently altering personal data elements to prevent the identification of individuals. In this case, the company obfuscated all personal data elements before training the model, which aligns with the definition of anonymization. This ensures that the data cannot be traced back to individuals, thereby protecting their privacy while still allowing the company to derive value from the dataset. Reference: AIGP Body of Knowledge, privacy-enhancing techniques section.

The correct answer is A. While educating technical staff is important, expecting all technical employees to be retooled as AI developers is unrealistic and not aligned with scalable governance practices.
From the AIGP ILT Guide:
"Training approaches should be role-specific and align with the individual's function and responsibilities...
Organizations typically do not expect every technical role to participate in model development." The AI Governance in Practice Report 2024 supports tailored approaches:
"Cross-functional training should be specific to the individual's role and exposure to AI risk... Role-based education supports scalability and comprehension." Thus, broad development training for all technical employees is the least practical and least likely approach.